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  <h1>Source code for federatedml.param.feature_selection_param</h1><div class="highlight"><pre>
<span></span><span class="ch">#!/usr/bin/env python</span>
<span class="c1"># -*- coding: utf-8 -*-</span>

<span class="c1">#</span>
<span class="c1">#  Copyright 2019 The FATE Authors. All Rights Reserved.</span>
<span class="c1">#</span>
<span class="c1">#  Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);</span>
<span class="c1">#  you may not use this file except in compliance with the License.</span>
<span class="c1">#  You may obtain a copy of the License at</span>
<span class="c1">#</span>
<span class="c1">#      http://www.apache.org/licenses/LICENSE-2.0</span>
<span class="c1">#</span>
<span class="c1">#  Unless required by applicable law or agreed to in writing, software</span>
<span class="c1">#  distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
<span class="c1">#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
<span class="c1">#  See the License for the specific language governing permissions and</span>
<span class="c1">#  limitations under the License.</span>
<span class="c1">#</span>
<span class="kn">from</span> <span class="nn">federatedml.param.base_param</span> <span class="k">import</span> <span class="n">BaseParam</span>
<span class="kn">from</span> <span class="nn">federatedml.param.feature_binning_param</span> <span class="k">import</span> <span class="n">FeatureBinningParam</span>
<span class="kn">from</span> <span class="nn">federatedml.util</span> <span class="k">import</span> <span class="n">consts</span>
<span class="kn">import</span> <span class="nn">copy</span>


<div class="viewcode-block" id="UniqueValueParam"><a class="viewcode-back" href="../../../federatedml.param.html#federatedml.param.feature_selection_param.UniqueValueParam">[docs]</a><span class="k">class</span> <span class="nc">UniqueValueParam</span><span class="p">(</span><span class="n">BaseParam</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Use the difference between max-value and min-value to judge.</span>

<span class="sd">    Parameters</span>
<span class="sd">    ----------</span>
<span class="sd">    eps: float, default: 1e-5</span>
<span class="sd">        The column(s) will be filtered if its difference is smaller than eps.</span>
<span class="sd">    &quot;&quot;&quot;</span>

    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="mf">1e-5</span><span class="p">):</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">eps</span> <span class="o">=</span> <span class="n">eps</span>

<div class="viewcode-block" id="UniqueValueParam.check"><a class="viewcode-back" href="../../../federatedml.param.html#federatedml.param.feature_selection_param.UniqueValueParam.check">[docs]</a>    <span class="k">def</span> <span class="nf">check</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="n">descr</span> <span class="o">=</span> <span class="s2">&quot;Unique value param&#39;s&quot;</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">check_positive_number</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">eps</span><span class="p">,</span> <span class="n">descr</span><span class="p">)</span>
        <span class="k">return</span> <span class="kc">True</span></div></div>


<div class="viewcode-block" id="IVValueSelectionParam"><a class="viewcode-back" href="../../../federatedml.param.html#federatedml.param.feature_selection_param.IVValueSelectionParam">[docs]</a><span class="k">class</span> <span class="nc">IVValueSelectionParam</span><span class="p">(</span><span class="n">BaseParam</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Use information values to select features.</span>

<span class="sd">    Parameters</span>
<span class="sd">    ----------</span>
<span class="sd">    value_threshold: float, default: 1.0</span>
<span class="sd">        Used if iv_value_thres method is used in feature selection.</span>

<span class="sd">    &quot;&quot;&quot;</span>

    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value_threshold</span><span class="o">=</span><span class="mf">0.0</span><span class="p">):</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">value_threshold</span> <span class="o">=</span> <span class="n">value_threshold</span>

<div class="viewcode-block" id="IVValueSelectionParam.check"><a class="viewcode-back" href="../../../federatedml.param.html#federatedml.param.feature_selection_param.IVValueSelectionParam.check">[docs]</a>    <span class="k">def</span> <span class="nf">check</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="n">descr</span> <span class="o">=</span> <span class="s2">&quot;IV selection param&#39;s&quot;</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">check_decimal_float</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">value_threshold</span><span class="p">,</span> <span class="n">descr</span><span class="p">)</span>
        <span class="k">return</span> <span class="kc">True</span></div></div>


<div class="viewcode-block" id="IVPercentileSelectionParam"><a class="viewcode-back" href="../../../federatedml.param.html#federatedml.param.feature_selection_param.IVPercentileSelectionParam">[docs]</a><span class="k">class</span> <span class="nc">IVPercentileSelectionParam</span><span class="p">(</span><span class="n">BaseParam</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Use information values to select features.</span>

<span class="sd">    Parameters</span>
<span class="sd">    ----------</span>
<span class="sd">    percentile_threshold: float, 0 &lt;= percentile_threshold &lt;= 1.0, default: 1.0</span>
<span class="sd">        Percentile threshold for iv_percentile method</span>


<span class="sd">    &quot;&quot;&quot;</span>

    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">percentile_threshold</span><span class="o">=</span><span class="mf">1.0</span><span class="p">):</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">percentile_threshold</span> <span class="o">=</span> <span class="n">percentile_threshold</span>

<div class="viewcode-block" id="IVPercentileSelectionParam.check"><a class="viewcode-back" href="../../../federatedml.param.html#federatedml.param.feature_selection_param.IVPercentileSelectionParam.check">[docs]</a>    <span class="k">def</span> <span class="nf">check</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="n">descr</span> <span class="o">=</span> <span class="s2">&quot;IV selection param&#39;s&quot;</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">check_decimal_float</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">percentile_threshold</span><span class="p">,</span> <span class="n">descr</span><span class="p">)</span>
        <span class="k">return</span> <span class="kc">True</span></div></div>


<div class="viewcode-block" id="VarianceOfCoeSelectionParam"><a class="viewcode-back" href="../../../federatedml.param.html#federatedml.param.feature_selection_param.VarianceOfCoeSelectionParam">[docs]</a><span class="k">class</span> <span class="nc">VarianceOfCoeSelectionParam</span><span class="p">(</span><span class="n">BaseParam</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Use coefficient of variation to select features. When judging, the absolute value will be used.</span>

<span class="sd">    Parameters</span>
<span class="sd">    ----------</span>
<span class="sd">    value_threshold: float, default: 1.0</span>
<span class="sd">        Used if coefficient_of_variation_value_thres method is used in feature selection. Filter those</span>
<span class="sd">        columns who has smaller coefficient of variance than the threshold.</span>

<span class="sd">    &quot;&quot;&quot;</span>

    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value_threshold</span><span class="o">=</span><span class="mf">1.0</span><span class="p">):</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">value_threshold</span> <span class="o">=</span> <span class="n">value_threshold</span>

<div class="viewcode-block" id="VarianceOfCoeSelectionParam.check"><a class="viewcode-back" href="../../../federatedml.param.html#federatedml.param.feature_selection_param.VarianceOfCoeSelectionParam.check">[docs]</a>    <span class="k">def</span> <span class="nf">check</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="n">descr</span> <span class="o">=</span> <span class="s2">&quot;Coff of Variances param&#39;s&quot;</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">check_positive_number</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">value_threshold</span><span class="p">,</span> <span class="n">descr</span><span class="p">)</span>
        <span class="k">return</span> <span class="kc">True</span></div></div>


<div class="viewcode-block" id="OutlierColsSelectionParam"><a class="viewcode-back" href="../../../federatedml.param.html#federatedml.param.feature_selection_param.OutlierColsSelectionParam">[docs]</a><span class="k">class</span> <span class="nc">OutlierColsSelectionParam</span><span class="p">(</span><span class="n">BaseParam</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Given percentile and threshold. Judge if this quantile point is larger than threshold. Filter those larger ones.</span>

<span class="sd">    Parameters</span>
<span class="sd">    ----------</span>
<span class="sd">    percentile: float, [0., 1.] default: 1.0</span>
<span class="sd">        The percentile points to compare.</span>

<span class="sd">    upper_threshold: float, default: 1.0</span>
<span class="sd">        Percentile threshold for coefficient_of_variation_percentile method</span>

<span class="sd">    &quot;&quot;&quot;</span>

    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">percentile</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">upper_threshold</span><span class="o">=</span><span class="mf">1.0</span><span class="p">):</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">percentile</span> <span class="o">=</span> <span class="n">percentile</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">upper_threshold</span> <span class="o">=</span> <span class="n">upper_threshold</span>

<div class="viewcode-block" id="OutlierColsSelectionParam.check"><a class="viewcode-back" href="../../../federatedml.param.html#federatedml.param.feature_selection_param.OutlierColsSelectionParam.check">[docs]</a>    <span class="k">def</span> <span class="nf">check</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="n">descr</span> <span class="o">=</span> <span class="s2">&quot;Outlier Filter param&#39;s&quot;</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">check_decimal_float</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">percentile</span><span class="p">,</span> <span class="n">descr</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">check_defined_type</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">upper_threshold</span><span class="p">,</span> <span class="n">descr</span><span class="p">,</span> <span class="p">[</span><span class="s1">&#39;float&#39;</span><span class="p">,</span> <span class="s1">&#39;int&#39;</span><span class="p">])</span>
        <span class="k">return</span> <span class="kc">True</span></div></div>


<div class="viewcode-block" id="FeatureSelectionParam"><a class="viewcode-back" href="../../../federatedml.param.html#federatedml.param.feature_selection_param.FeatureSelectionParam">[docs]</a><span class="k">class</span> <span class="nc">FeatureSelectionParam</span><span class="p">(</span><span class="n">BaseParam</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Define the feature selection parameters.</span>

<span class="sd">    Parameters</span>
<span class="sd">    ----------</span>
<span class="sd">    select_cols: list or int, default: -1</span>
<span class="sd">        Specify which columns need to calculated. -1 represent for all columns.</span>

<span class="sd">    filter_methods: list, [&quot;unique_value&quot;, &quot;iv_value_thres&quot;, &quot;iv_percentile&quot;,</span>
<span class="sd">                &quot;coefficient_of_variation_value_thres&quot;, &quot;outlier_cols&quot;],</span>
<span class="sd">                 default: [&quot;unique_value&quot;]</span>

<span class="sd">        Specify the filter methods used in feature selection. The orders of filter used is depended on this list.</span>
<span class="sd">        Please be notified that, if a percentile method is used after some certain filter method,</span>
<span class="sd">        the percentile represent for the ratio of rest features.</span>

<span class="sd">        e.g. If you have 10 features at the beginning. After first filter method, you have 8 rest. Then, you want</span>
<span class="sd">        top 80% highest iv feature. Here, we will choose floor(0.8 * 8) = 6 features instead of 8.</span>

<span class="sd">        unique_value: filter the columns if all values in this feature is the same</span>

<span class="sd">        iv_value_thres: Use information value to filter columns. If this method is set, a float threshold need to be provided.</span>
<span class="sd">            Filter those columns whose iv is smaller than threshold.</span>

<span class="sd">        iv_percentile: Use information value to filter columns. If this method is set, a float ratio threshold</span>
<span class="sd">            need to be provided. Pick floor(ratio * feature_num) features with higher iv. If multiple features around</span>
<span class="sd">            the threshold are same, all those columns will be keep.</span>

<span class="sd">        coefficient_of_variation_value_thres: Use coefficient of variation to judge whether filtered or not.</span>

<span class="sd">        outlier_cols: Filter columns whose certain percentile value is larger than a threshold.</span>

<span class="sd">    &quot;&quot;&quot;</span>

    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">select_cols</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span> <span class="n">filter_methods</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">local_only</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
                 <span class="n">unique_param</span><span class="o">=</span><span class="n">UniqueValueParam</span><span class="p">(),</span>
                 <span class="n">iv_value_param</span><span class="o">=</span><span class="n">IVValueSelectionParam</span><span class="p">(),</span>
                 <span class="n">iv_percentile_param</span><span class="o">=</span><span class="n">IVPercentileSelectionParam</span><span class="p">(),</span>
                 <span class="n">variance_coe_param</span><span class="o">=</span><span class="n">VarianceOfCoeSelectionParam</span><span class="p">(),</span>
                 <span class="n">outlier_param</span><span class="o">=</span><span class="n">OutlierColsSelectionParam</span><span class="p">(),</span>
                 <span class="n">need_run</span><span class="o">=</span><span class="kc">True</span>
                 <span class="p">):</span>
        <span class="nb">super</span><span class="p">(</span><span class="n">FeatureSelectionParam</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">select_cols</span> <span class="o">=</span> <span class="n">select_cols</span>
        <span class="k">if</span> <span class="n">filter_methods</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">filter_methods</span> <span class="o">=</span> <span class="p">[</span><span class="n">consts</span><span class="o">.</span><span class="n">UNIQUE_VALUE</span><span class="p">]</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">filter_methods</span> <span class="o">=</span> <span class="n">filter_methods</span>

        <span class="bp">self</span><span class="o">.</span><span class="n">local_only</span> <span class="o">=</span> <span class="n">local_only</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">unique_param</span> <span class="o">=</span> <span class="n">copy</span><span class="o">.</span><span class="n">deepcopy</span><span class="p">(</span><span class="n">unique_param</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">iv_value_param</span> <span class="o">=</span> <span class="n">copy</span><span class="o">.</span><span class="n">deepcopy</span><span class="p">(</span><span class="n">iv_value_param</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">iv_percentile_param</span> <span class="o">=</span> <span class="n">copy</span><span class="o">.</span><span class="n">deepcopy</span><span class="p">(</span><span class="n">iv_percentile_param</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">variance_coe_param</span> <span class="o">=</span> <span class="n">copy</span><span class="o">.</span><span class="n">deepcopy</span><span class="p">(</span><span class="n">variance_coe_param</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">outlier_param</span> <span class="o">=</span> <span class="n">copy</span><span class="o">.</span><span class="n">deepcopy</span><span class="p">(</span><span class="n">outlier_param</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">need_run</span> <span class="o">=</span> <span class="n">need_run</span>

<div class="viewcode-block" id="FeatureSelectionParam.check"><a class="viewcode-back" href="../../../federatedml.param.html#federatedml.param.feature_selection_param.FeatureSelectionParam.check">[docs]</a>    <span class="k">def</span> <span class="nf">check</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="n">descr</span> <span class="o">=</span> <span class="s2">&quot;hetero feature selection param&#39;s&quot;</span>

        <span class="bp">self</span><span class="o">.</span><span class="n">check_defined_type</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">filter_methods</span><span class="p">,</span> <span class="n">descr</span><span class="p">,</span> <span class="p">[</span><span class="s1">&#39;list&#39;</span><span class="p">])</span>

        <span class="k">for</span> <span class="n">idx</span><span class="p">,</span> <span class="n">method</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">filter_methods</span><span class="p">):</span>
            <span class="n">method</span> <span class="o">=</span> <span class="n">method</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">check_valid_value</span><span class="p">(</span><span class="n">method</span><span class="p">,</span> <span class="n">descr</span><span class="p">,</span> <span class="p">[</span><span class="s2">&quot;unique_value&quot;</span><span class="p">,</span> <span class="s2">&quot;iv_value_thres&quot;</span><span class="p">,</span> <span class="s2">&quot;iv_percentile&quot;</span><span class="p">,</span>
                                              <span class="s2">&quot;coefficient_of_variation_value_thres&quot;</span><span class="p">,</span>
                                              <span class="s2">&quot;outlier_cols&quot;</span><span class="p">])</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">filter_methods</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span> <span class="o">=</span> <span class="n">method</span>
        <span class="c1"># if &quot;iv_value_thres&quot; in self.filter_method and &quot;iv_percentile&quot; in self.filter_method:</span>
        <span class="c1">#     raise ValueError(&quot;Two iv methods should not exist at the same time.&quot;)</span>

        <span class="bp">self</span><span class="o">.</span><span class="n">check_defined_type</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">select_cols</span><span class="p">,</span> <span class="n">descr</span><span class="p">,</span> <span class="p">[</span><span class="s1">&#39;list&#39;</span><span class="p">,</span> <span class="s1">&#39;int&#39;</span><span class="p">])</span>

        <span class="bp">self</span><span class="o">.</span><span class="n">check_boolean</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">local_only</span><span class="p">,</span> <span class="n">descr</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">unique_param</span><span class="o">.</span><span class="n">check</span><span class="p">()</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">iv_value_param</span><span class="o">.</span><span class="n">check</span><span class="p">()</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">iv_percentile_param</span><span class="o">.</span><span class="n">check</span><span class="p">()</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">variance_coe_param</span><span class="o">.</span><span class="n">check</span><span class="p">()</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">outlier_param</span><span class="o">.</span><span class="n">check</span><span class="p">()</span></div></div>

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